Real Time Gender Classification Based on Facial Features Using EBGM

  • D. K. Kishore Galla
  • BabuReddy MukamallaEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Presently a day’s face acknowledgment is an effect theme in some of security issues introduces progressively applications. In light of every day utilization gadgets, secure shortage is an escalated application in confront extraction. Generally create Principle Component Analysis (PCA) based face acknowledgment in picture preparing, in this they are utilizing skin shading based approach for include extraction and face acknowledgment to enhance the precision of the application. In any case, is it not available for dimensional component extraction in confronting acknowledgment. So in this document, we propose a new & novel approach i.e. Elastic Bunch Graph Matching (EBGM), in highlight extraction to order tight and wide weed utilizing SIFT key-focuses descriptor. Specifically we break down the SIFT key components of weed pictures and outline a calculation to remove the element vectors of SIFT key-focuses in view of extent and edge course. Scale Invariant Feature Transform (SIFT) turned out to be the most vigorous neighbourhood variable component descriptor. Filter based method for recognizing and extricating nearby component and expressive descriptors which are sensibly changes in enlightenment, picture commotion, revolution & scaling and little changes in perspective. Our experimental results show efficient face recognition for real time image processing applications.


Image processing Recognition of face Invariant scale feature transform Analysis of principle component and dynamic and specific algorithms 


  1. 1.
    Wiskott L, Fellous JM, Kuiger N, von der Malsburg (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19:775–779CrossRefGoogle Scholar
  2. 2.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  3. 3.
    Ravela S, Hanson A (2001) On multi-scale differential features for face recognition. Proc. Vision Interface, pp 15–21Google Scholar
  4. 4.
    Yanushkevich S, Hurley D, Wang P (2008) Editorial. Special Issue on Pattern Recognition and Artificial Intelligence in Biometrics (IJPRAI) 22(3):367–369Google Scholar
  5. 5.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  6. 6.
    Lowe D (1999) Object recognition from local scale-invariant features. Int Conf Comput Vision 90:150–1157Google Scholar
  7. 7.
    Lowe D (2001) Local feature view clustering for 3d object recognition. IEEE Conf Comput Vision Pattern Recognit 1:682–688Google Scholar
  8. 8.
    Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. IEEE Conf Comput Vision Pattern Recognit 4:506–513Google Scholar
  9. 9.
    Brown M, Lowe D (2003) Recognising panoramas. IEEE Int. Conf. Comput Vision 3:1218–1225CrossRefGoogle Scholar
  10. 10.
    Abdel-Hakim A, Farag A (2006) CSIFT: A SIFT descriptor with color invariant characteristics. In: Proceedings of the 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 2, pp 1978–1983Google Scholar
  11. 11.
    Bicego M, Lagorio A, Grosso E, Tistarelli M (2006) On the use of SIFT features for face authentication. In: Proceedings of IEEE Int Workshop on Biometrics, in Association with CVPR, pp 35–41, NYGoogle Scholar
  12. 12.
    Luo J, Ma Y, Takikawa E, Lao SH, Kawade M, Lu BL (2007) Person-specific SIFT features for face recognition. In: International conference on acoustic, speech and signal processing (ICASSP 2007), Hawaii, pp 563–566Google Scholar
  13. 13.
    KreBel U (1999) Pairwise classification and support vector machines. In: Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 255–268Google Scholar
  14. 14.
    Hen YM, Khalid M, Yusof R (2007) Face verification with Gabor representation and support vector machines. In: Proceedings of the first Asia international conference on modelling & simulation, pp 451–459Google Scholar
  15. 15.
    Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New YorkCrossRefGoogle Scholar
  16. 16.
  17. 17.
    Sanguansat P, Asdornwised W, Jitapunkul S, Marukatat S (2006) Class-specific subspace-based two-dimensional principal component analysis for face recognition. In: Proceedings of the 18th international conference on pattern recognition (ICPR), vol 2, pp 1246–1249Google Scholar
  18. 18.
  19. 19.
    Zheng YJ, Yang JY, Yang J, Wu XJ, Yu DJ (2006) A complete and rapid feature extraction method for face recognition. In: Proceedings of the 18th international conference on pattern recognition (ICPR), vol 3, pp 469–472Google Scholar
  20. 20.
    Nazeer SA, Omar N, Khalid M (2007) Face recognition system using artificial neural networks approach. In: IEEE conference on ICSCN 2007, MIT Campus, Anna University, Chennai, India, February 22–24, pp 420–425Google Scholar
  21. 21.
    Kishore GDK (2017) A literature survey on object classification techniques. Int J Adv Technol Eng Sci 5(3):779–786Google Scholar
  22. 22.
    Kishore GDK, Babu Reddy M (2017) Comparative analysis between classification algorithms and data sets (1: N & N:1) through WEKA. Open Access Int J Sci Eng 2(5):23–28Google Scholar
  23. 23.
    Kishore GDK, Babu Reddy M (2018) Analysis and prototype sequences of face recognition techniques in real-time picture processing. Intelligent engineering informatics, advances in intelligent systems and computing, vol 695. Springer, SingaporeGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Krishna UniversityMachilipatnamIndia

Personalised recommendations